Vehicle emission control systems may be configured to store fuel vapors from fuel tank refueling and diurnal engine operations, and then purge the stored vapors during a subsequent engine operation. In an effort to meet stringent federal emissions regulations, emission control systems may need to be intermittently diagnosed for the presence of undesired vapor emissions that could release fuel vapors to the atmosphere. Undesired vapor emissions may be identified using engine-off natural vacuum (EONV) during conditions when a vehicle engine is not operating. In particular, a fuel system may be isolated at an engine-off event. The pressure in such a fuel system may increase if the tank is heated further (e.g., from hot exhaust or a hot parking surface) as liquid fuel vaporizes. A pressure rise above a threshold may indicate the absence of undesired fuel system vapor emissions. Alternatively, in the absence of a pressure rise above a threshold, as a fuel system cools down, a vacuum is generated therein as fuel vapors condense to liquid fuel. Vacuum generation may monitored and undesired fuel system vapor emissions identified based on expected vacuum development or expected rates of vacuum development.
Entry conditions and thresholds for an EONV test may be based on an inferred total amount of heat rejected into the fuel tank during the prior drive cycle. The inferred amount of heat may be based on engine run-time, integrated mass air flow, fuel level, ambient temperature, reid vapor pressure, etc. While these heat rejection inferences work well in most conditions, they may be prone to errors when noise factors are involved. For example, if a vehicle is driven downhill for an extended period, driven under rainy and/or windy conditions, or under conditions where a period of high-speed driving is followed by a period of idling, much of the heat rejection to the fuel tank may be negated. As a result, an EONV test executed based on a heat rejection inference where the above described noise factors are involved may result in a false failure.
As a safeguard to potential error in EONV test results, the California Air Resources Board (CARB) allows original equipment manufacturers (OEMs) to use an Exponentially Weighted Moving Average (EWMA) to set a malfunction indicator light (MIL). EWMA filtering is a data processing technique used to calculate a filtered value based on raw data points collected from an incoming stream of data, for example data resulting from a number of EONV tests. In the event that the calculated filtered value determined by the EWMA filter exceeds a threshold, a diagnostic trouble code (DTC) may be set to a fail status, and a MIL may be illuminated to indicate the fault. As an example, the normal EWMA (NORM) filter in use is a heavily filtered channel that is enabled after a fourth EONV test. Per CARB's requirements, it will illuminate a MIL in one trip, wherein using the recommended filter constant will produce filtering comparable to a five-test average. Use of this normal filter screens out minor EONV failures associated with the noise factors described above.
However, results from EONV tests all go through EWMA filtering with constant rates not considering the degree of compliance to entry conditions. This is because the final outcome of evaluation of a set of entry conditions results in either enabling an EONV test, or prohibiting the EONV test from executing. For example, if one or more entry conditions do not pass a threshold, the EONV test execution may be prohibited. On the other hand, if all entry conditions pass a threshold, the EONV test may be enabled. As such, upon completion of an EONV test, the EWMA filtered result may in fact be quite misleading because the confidence for each EONV test run is not constant, yet the results are all treated with equal weighting through EWMA filtering. For example, if one or more entry conditions are very near their thresholds, an EONV test may execute, yet the degree of confidence in the result may be less than the degree of confidence for an EONV test where the same entry conditions are substantially above their thresholds.
An alternative to equally weighting an EWMA filter is to make the weighting variable to reflect a confidence level in the EONV test results. A method for increasing a weighting factor of an EWMA filter is disclosed in U.S. Patent Application Publication No. 2014/0122020 A1. Therein, the method includes increasing the weighting factor of the EWMA filter to more heavily weigh incoming raw data values of the data stream. However, the inventors herein have recognized that while teaching adjusting the weighting factor of an EWMA filter, such a method does not teach adjusting the weighting factor based on a level of confidence in an obtained test result.
The inventors herein have recognized the above issues, and have developed systems and methods to at least partially address them. In one example, a method is provided comprising, inducting vapors from a fuel system into an engine which propels a motor vehicle, and, responsive to an engine shut-down event, evaluating one or more EONV test entry conditions with fuzzy membership functions and/or calibratable tables corresponding to predicted outcomes of an EONV test, the predicted outcomes comprising, for example, undesired vapor emissions and the absence of undesired vapor emissions. Entry conditions may be based on an inferred total amount of heat rejected into a fuel tank during a previous drive cycle, and the inferred amount of heat may be based on one or more of the following: engine run-time, integrated mass air flow, fuel level, ambient temperature, reid vapor pressure, etc. For each entry condition, a maximum confidence value, or degree of confidence, for each predicted outcome may be determined, and then a maximum temporary overall confidence value may further be determined based on all individual maximum confidence values. In one example, the method may include generating a degree of confidence for each of a plurality of test entry conditions that a test for undesired vapor emissions will achieve a reliable result, and commencing the test based on a temporary confidence value related to the degree of confidences. In this way, an optimistic interpretation of entry conditions may be used to enable entry into the EONV evaporative emissions test such that the test executes frequently.
As one example, subsequent to the generation of an EONV test result based on pressure of the fuel system during the test, a second set of confidence values, or degree of confidences, may be obtained based on the fuzzy membership function and/or calibratable table for each individual entry condition corresponding to the EONV test result, and then a final overall confidence value may be further determined based on all second individual confidence values. In one example, the final overall confidence value may be an average of the individual confidence values. The final overall confidence value may then be used to modify a weighting factor for an EWMA filter. Thus, rather than equally weighting the EWMA filter, the weighting is made variable such that the output reflects the level of confidence in the EONV test results. As such, higher confidence results may be more heavily weighted than those results with lower confidence. In this way, the setting of a MIL when undesired emissions are indicated but the confidence level in the test results are low, may be reduced. Additionally, the early setting of a MIL when undesired emissions are indicated with high confidence, may contribute to a reduction in undesired evaporative emissions.
The above advantages and other advantages, and features of the present description will be readily apparent from the following Detailed Description when taken alone or in connection with the accompanying drawings.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.